Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO methods are most effective for improving factual accuracy in a language model's responses? (Choose two.)
⚠ Common exam trap
A common misconception is that reducing randomness (temperature) or increasing token diversity (top-k) directly improves factual accuracy, when in fact these parameters affect output style and creativity, not the correctness of the underlying facts.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use prompt engineering to instruct the model to rely on provided facts.
Prompt engineering can explicitly instruct the model to base its responses on provided facts, reducing reliance on parametric knowledge that may be outdated or incorrect. By including directives like 'Use only the information in the following text' or 'Answer based solely on the provided context,' the model is guided to prioritize given facts over its internal training data, which improves factual accuracy in the output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use prompt engineering to instruct the model to rely on provided facts.
Why this is correct
Prompt engineering can explicitly direct the model to verify claims or stick to given knowledge.
- ✗
Decrease the temperature to make responses more deterministic.
Why it's wrong here
Deterministic does not guarantee correctness; the model may confidently output wrong facts.
- ✗
Increase top-k sampling to consider a wider range of tokens.
Why it's wrong here
Top-k adds randomness and may lead to fabricated facts.
- ✗
Replace the model with a smaller, more focused model.
Why it's wrong here
Smaller models typically have less knowledge and lower factual accuracy.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a trusted knowledge base.
Why this is correct
RAG retrieves current, verifiable information to ground the model's responses.
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